Multilevel bit-mapped image analysis method

ABSTRACT

The present invention discloses a multilevel method of bitmapped image analysis that comprises a whole image data representation via its components—objects of different levels of complexity—hierarchically connected therebetween by spatially-parametrical links. The said method comprises preliminarily generating a classifier of the objects that possibly may be present in the image consisting of one or more levels differing in complexity; parsing the image into objects; attaching each object to one of predetermined levels; establishing hierarchical links between objects of different levels; establishing links between objects within the same level; and performing an object feature analysis. The objects feature analysis comprises at least generating and examining a hypothesis about object features and correcting the object&#39;s features of the same and other levels in response to the hypothesis examination results. The step of object features analysis may also comprise execution of a recursive RX-cut within the same level.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to a field of bit-mapped imagecharacter recognition systems and, more particularly, to a method ofparsing and analysis pre-process assisting character and textrecognition of printed text from a bit-mapped binary image or otherbinary or raster images inputted from a scanning device or the like orobtained by other means.

2. Prior Art

Segmentation and parsing methods are known in the art. Typically, suchmethods divide an image into parcels containing homogeneous objects anduse a plurality of special computing procedures, each depending on aplurality of parameters, to analyze an object.

Known segmentation and parsing methods give little ability to perform afully complete and overall analysis of objects since they use deficientdata. Moreover, said methods require a large number of specializedcomputing procedures for analysis that depends on the quantity ofobjects multiplied by the number of parameters thereof.

An example of said type of systems is described in U.S. Pat. No.6,408,094 Jun. 18, 2002.

Another known kind of method that parses a bit-mapped image into regionswith further links startup between objects within the same level andbetween levels of complexity is described in [1] and U.S. Pat No.6,038,342 Mar. 14, 2000; U.S. Pat. No. 5,848,184 Dec. 8, 1998.

The main limitations of these methods are a great number of specializedcomputing procedures required for each of a plurality of object typesand a scantiness of obtained subsidiary data that is insufficient andcannot be used to analyze other objects.

Another method of pre-processing before character and text recognitionof a printed text obtained from a bit mapped image requires inputtingscanned data into a bit-mapped file, parsing it into objects and afurther analysis of each type of object by a specific computing means.The essential feature of the method lies in its ability to operate onlywith shape parameters thereof. The method cannot perform a fullycomplete and cannot perform overall analysis as it uses no spatialparameters data (U.S. Pat. No. 5,594,815 Jan. 14).

Therefore, the target of the present invention is to develop abit-mapped image analysis method, not requiring considerable analysismeans, obtaining more informative analysis results and higher accuracythan the prior art.

SUMMARY OF THE INVENTION

A multilevel bit-mapped image analysis method is described.

The present invention discloses a method of preliminary multilevelanalysis of a bit-mapped image, obtained from a scanner or the like orfrom any other source. The analysis according to the present inventioncomprises whole image data representation via its components—objects ofdifferent levels of complexity—hierarchically connected therebetween byspatially-parametrical links. In particular the analysis according tothe present invention comprises preliminarily classifying all the textand non-text objects that can be present in a document into severallevels of complexity, parsing the image into a plurality of regions andobjects, attaching every object found in the image to one of thepredefined set of levels differing in complexity, performing an analysisof the obtained regions and objects, extracting subsidiary datatherefrom and using the said data in analysis, and further outputtingthe results to a subsequent process, typically to a characterrecognition process or the like.

The present invention further discloses a method of making this processmore informative, increasing analysis and recognition accuracy, reducinga required computing apparatus, and saving system resources.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 and FIG. 2 are block diagrams showing some possible samples ofclassifiers that may be generated in practice. The set, number and thecontents of levels can differ greatly from those shown here.

FIG. 3 is a block diagram showing the uniform list of steps to be madeon analysis of object of any level according to the present invention.

DETAILED DESCRIPTION OF THE INVENTION

A method of present invention consists in a preliminary analysis of abit-mapped image and extracting and utilizing the maximum possibleamount of supplemental data for the sake of the said analysis.

The most widely known methods deal with parsing of an initial image intoobjects and further applying a plurality of analyzing means to eachparsed object. Thus, the set of computing means for analysis mayincrease greatly, resulting in an enormous increase in complexity of thewhole system.

Most of the known methods use little or none supplemental data, thuslosing in accuracy.

The main principle of present invention consists in representing of allbit-mapped image data as a multilevel model, where more complicatedobjects are defined via their composing less complicated components, intheir turn linked therebetween by spatially-parametrical correlations.

Utilizing the method of the present invention, all types of objects thatmay appear in the analyzed image are preliminarily classified intoseveral levels, differing in complexity. The set of possible levelsdepends greatly on the layout of the initial bit-mapped image. It canconsist of text only, or text with tables, or formatted text withpictures (non-text regions) and/or tables, etc. In each case theclassifier will comprise a different set of levels.

After that the whole image is parsed into objects, that is regions,tables, paragraphs, lines, characters, etc.

Then each object is attached to one of the predetermined levels ofcomplexity.

A system of links is established between the objects within each level.

A system of links is established between the objects of differentlevels.

The analysis can be started at the highest level of complexity or at thelowest level or at any intermediate level depending on the appliedstrategy.

The object's features comprise at least a list of objects of lowercomplexity incorporated into the object and a relationship factorsestimation of objects within the same level of complexity. Theestimation must comprise at least spatial estimations and parametricalestimations.

An important point for utilizing the method of the present invention isthat the analysis on any level should comprise at least the steps ofsetting up a hypothesis about the features of an object, examining thehypothesis about the features of the object and correcting the concernedobjects' features of the same and other levels depending on thehypothesis examination results.

To obtain subsidiary data on objects features, a recursive RX-cut onobjects can be executed on any level. This can sometimes get moresubsidiary data concerning the objects' features, and can confirm ahypothesis concerning the objects of any other level.

The method of a recursive RX-cut is known from prior art. It consists inparsing an image into non-intersected regions within one level ofcomplexity, each region including objects with close spatial andparametrical features. For each region a set of features common to allobjects of the region is defined. Then a complete analysis of allregions is performed. Feature restrictions or extensions concerningfeatures common for the regions are added to each region. Thecorresponding features amendments are made. In the case of anyconsiderable discrepancy between initial and new features of an object,it can be eliminated from the region. As a result of recursive RX-cutanalysis some regions with close features, separated by non-textobjects, can be combined into a new one. The main result of the analysisand combination of regions lies in obtaining a new volume of subsidiarydata on objects' features.

Referring now to FIG. 1 and FIG. 2, the structure of the classifier canvary greatly and depends mainly upon the image layout, the set ofpossible objects in the image, targets of analysis, etc.

FIG. 3 shows the uniform list of analyzing steps that should be appliedaccording to the present invention to each object of every level in thecourse of the analysis process.

Block 1—forming a hypothesis about an object's features; block2—examining the hypothesis about the object's features; block3—correcting the concerned objects' features of the same and otherlevels; block 4—executing at least one recursive RX-cut on objects.

Thus the method of the present invention allows us to apply subsidiarydata for analysis, to decrease the necessary set of computing means, toraise accuracy and reduce mistakes in analysis and subsequent processes.

REFERENCES CITED

-   1.A. Jam and Y. Zhong, “Page Segmentation Using Texture Analysis,”    Pattern Recognition, vol. 29, pp. 743-770, 1996.-   2.J. Ha, R. Haralick, and I. Phillips, “Recursive X-Y Cut Using    Bounding Boxes of Connected Components,” Proc. Third Int'l Conf.    Document Analysis and Recognition, pp. 952-955, 1995.-   3.J, Ha, R. Haralick, and I, Phillips, “Document Page Decomposition    by the Bounding-Box Projection Technique,” Proc. Third Int'l Conf.    Document Analysis and Recognition, pp. 11191122, 1995.

1. A method of bit-mapped image analysis comprising: classifying typesof objects that are present within a bit-mapped image into one or morelevels differing in complexity; parsing the said bit-mapped image intoobjects attaching every said object to one of said levels of complexity;establishing links between said objects of said different levels ofcomplexity; establishing links between said objects within each level ofcomplexity; performing an object feature analysis, said object featureanalysis comprising: forming a hypothesis about a spatial feature or aparametrical feature of the object, wherein the spatial feature or theparametrical feature is associated with a level of complexity, examiningthe said hypothesis about the spatial feature or the parametricalfeature of the object, and when necessary, correcting the spatialfeature or the parametrical feature of the object of the same and otherlevels depending on a result from the said examining of the hypothesis.2. The method as recited in claim 1, wherein said object featureanalysis further comprises execution of at least one recursive RX-cut onobjects within a same level of complexity.
 3. The method as recited inclaim 1, wherein said hypothesis about a spatial feature or aparametrical feature of the object comprises at least a list of objectsof lower complexity incorporated into the object, and wherein the methodfurther comprises outputting the results to a subsequent process.
 4. Themethod as recited in claim 3, wherein said bit-mapped image analysis isperformed by moving from a higher complexity level to a lower complexitylevel and wherein the subsequent process is a character recognitionprocess.
 5. The method as recited in claim 1, wherein said bit-mappedimage analysis is performed by moving from a lower complexity level to ahigher complexity level, and wherein the spatial feature or theparametrical feature of the object includes at least the following data:a list of objects of lower complexity incorporated into the objectsubject to the object feature analysis, and a relationship factorestimation of objects within a same level of complexity, saidrelationship factor estimation including at least a spatial estimationand a parametrical estimation.
 6. The method as recited in claim 1,wherein said bit-mapped image analysis is performed by applying themethod to any level and further moving to any level in an order and anumber of times as determined by the bit-mapped image analysis, andwherein the bit-mapped image analysis is performed by moving from ahigher complexity level to a lower complexity level.
 7. The method asrecited in claim 1, wherein said bit-mapped image analysis is performedby moving from a lower level of complexity to a higher level ofcomplexity.
 8. The method as recited in claim 1, wherein said bit-mappedimage analysis is performed by applying the bit-mapped image analysis toa given level of complexity and subsequently applying it to a lowerlevel of complexity or to a higher level of complexity.
 9. The method asrecited in claim 1, wherein the levels of complexity include characters,word tokens, words, lines, paragraphs, regions, tables, and non-textregions.
 10. The method as recited in claim 9, wherein after examining ahypothesis at least at one level of complexity, performing a recursiveRX-cut to obtain subsidiary data about a spatial feature or aparametrical feature of the same and other levels.